The system adds automated evaluators that verify answers by AI models
Google said AlphaEvolve has enhanced the efficiency of its data centres
AlphaEvolve uses Gemini Flash and Gemini Pro in tandem
Google is working on a user interface for AlphaEvolve
Photo Credit: Google
Google DeepMind announced a new artificial intelligence (AI) coding agent on Wednesday that can enhance the capabilities of AI models. Dubbed AlphaEvolve, it is designed to discover and optimise algorithms across complex computing and mathematical tasks. The powerful AI system is built on the Mountain View-based tech giant's Gemini models, and it combines outputs generated by large language models with automated evaluators to ground the responses in reality and reduce the risk of hallucinations. Beyond this, the system is also said to have shown potential in solving and optimising mathematical problems.
Google DeepMind Introduces AlphaEvolve Coding Agent
In a blog post, DeepMind detailed the new technology it has been working on. AlphaEvolve is not an AI model, instead, it is a complex AI system with agentic capabilities. One of the primary functions the system performs is algorithm discovery and optimisation.
AI models, at a fundamental level, are a series of code. These code bases process and compile information, break it down, and use probabilistic algorithms to generate an output. However, since AI systems are highly complex, their code bases are massive. This large size often causes optimisation and efficiency-based issues. AlphaEvolve can help with that, the company said.
AlphaEvolve uses automated evaluation metrics, and using these parameters, it verifies, runs, and scores responses generated by AI models. Google said this method allows the system to quantifiably assess responses from multiple AI models and reduce the risk of hallucinations. Additionally, the system can also fix and improve code that allows such hallucinations.
The tech giant said that AlphaEvolve has improved the efficiency of Google's data centres, chip design, and AI training processes. Interestingly, it was also able to improve the training of its own base LLM. In one case, it discovered a new scheduling method that recovers around 0.7 percent of Google's global compute resources — a massive gain when applied across the company's massive infrastructure.
Since AlphaEvolve works with code bases and algorithms, it is also said to have high potential in different areas of mathematical problem solving, the company said. It is said to have discovered a faster method to multiply 4x4 complex matrices, beating a solution that had stood for more than 50 years. In tests across 50 open mathematical problems, AlphaEvolve matched the current best solutions in most cases, and even improved on them in about 20 percent of problems, the post added.
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